1. Technical Field
This application generally relates to the field of improving hybrid vehicle fuel efficiency and, in particular, to improving hybrid vehicle fuel efficiency using inverse reinforcement learning.
2. Background Information
A Hybrid Electric Vehicle (HEV) combines a conventional internal combustion engine propulsion system (which runs on fuel) with an electric propulsion system (which runs on battery charge). The presence of the electric powertrain enables the HEV to achieve better fuel economy. Fuel usage is reduced by using an electric storage system to save part of the energy that is produced by the engine and by regenerative braking.
At any time, the proportion of electric energy and engine energy that is being used by the HEV can be optimized to improve fuel efficiency. For example, if it is known that the upcoming driving route has stop-and-go traffic with red lights, then there will be opportunities to recharge the electric battery from regenerative braking, and it is advantageous to use power from the battery. Similarly, if speed is low and the engine efficiency is better at higher revolutions-per-minute (RPM), then it may be advantageous to run the engine at higher RPM and save the extra energy in the battery.
A system controls an HEV's powertrain regarding what mix of engine power and battery power is used. The control policy implemented by the powertrain control system is critical to the HEV's fuel efficiency. Some control policies consider several factors such as the charge of the battery and how efficiently the engine operates at a given speed. These approaches do not take into account the future power requirements of the HEV. By taking into account future power requirements, a more efficient balance of engine power usage versus battery power usage can be attained. For example, fuel can be saved if the future driving route of the HEV is known. However, most of the time the destination of the HEV is unknown a priori.